Abstract
PURPOSE: Discrete choice experiment (DCE) and profile case (case 2) best-worst scaling (BWS) present uncertainties regarding the acceptability of quantifying individual healthcare preferences, which may adversely affect the validity of responses and impede the reflection of true healthcare preferences. This study aimed to assess the acceptability of these two methods from the perspective of patients with type 2 diabetes mellitus (T2DM) and examine their association with specific characteristics of the target population. PATIENTS AND METHODS: This cross-sectional study was based on a nationally representative survey; data were collected using a multistage stratified cluster-sampling procedure between September 2021 and January 2022. Eligible adults with confirmed T2DM voluntarily participated in this study. Participants completed both the DCE and case 2 BWS (BWS-2) choice tasks in random order and provided self-reported assessments of acceptability, including task completion difficulty, comprehension of task complexity, and response preference. Logistic regression and random forest models were used to identify variables associated with acceptability. RESULTS: In total, 3286 patients with T2DM were included in the study. Respondents indicated there was no statistically significant difference in completion difficulty between the DCE and BWS-2, although the DCE scores were slightly higher (3.07 ± 0.68 vs 3.03 ± 0.67, P = 0.06). However, 1979 (60.2%) respondents found the DCE easier to comprehend. No significant preferences were observed between the two methods (1638 (49.8%) vs 1648 (50.2%)). Sociodemographic factors, such as residence, monthly out-of-pocket costs, and illness duration were significantly associated with comprehension complexity and response preference. CONCLUSION: This study yielded contrasting results to most of previous studies, suggesting that DCE may be less cognitively demanding and more suitable for patients with T2DM from the perspective of self-reported acceptability of DCE and BWS. This study promotes a focus on patient acceptability in quantifying individual healthcare preferences to inform tailored optimal stated-preference method for a target population.